ESA2021

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</svg> Nina Fárová, <svg viewBox="0 0 448 512" style="height:1em;fill:currentColor;position:relative;display:inline-block;top:.1em;" xmlns="http://www.w3.org/2000/svg">
  <path d="M224 256c70.7 0 128-57.3 128-128S294.7 0 224 0 96 57.3 96 128s57.3 128 128 128zm95.8 32.6L272 480l-32-136 32-56h-96l32 56-32 136-47.8-191.4C56.9 292 0 350.3 0 422.4V464c0 26.5 21.5 48 48 48h352c26.5 0 48-21.5 48-48v-41.6c0-72.1-56.9-130.4-128.2-133.8z"></path>
</svg> Radim Hladík
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</svg> @hlageek
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</svg> hladik@flu.cas.cz
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</svg> ESA 2021
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</svg> Barcelona (virtual)
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</svg> 2021, September 1
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# .left[**Topical Gender Segregation Among Grant Recipients**]Scientific Careers and Practices I

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.center[

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<img src="https://www.europeansociology.org/sites/default/files/2020-10/esa_logo2_1_1.png" height="300">
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<svg viewBox="0 0 448 512" style="height:1em;fill:currentColor;position:relative;display:inline-block;top:.1em;" xmlns="http://www.w3.org/2000/svg">
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</svg> esa2021

]

.pull-right.left[

### Nina Fárová, Radim Hladík
<svg viewBox="0 0 512 512" style="height:1em;fill:currentColor;position:relative;display:inline-block;top:.1em;" xmlns="http://www.w3.org/2000/svg">
  <path d="M464 64H48C21.49 64 0 85.49 0 112v288c0 26.51 21.49 48 48 48h416c26.51 0 48-21.49 48-48V112c0-26.51-21.49-48-48-48zm0 48v40.805c-22.422 18.259-58.168 46.651-134.587 106.49-16.841 13.247-50.201 45.072-73.413 44.701-23.208.375-56.579-31.459-73.413-44.701C106.18 199.465 70.425 171.067 48 152.805V112h416zM48 400V214.398c22.914 18.251 55.409 43.862 104.938 82.646 21.857 17.205 60.134 55.186 103.062 54.955 42.717.231 80.509-37.199 103.053-54.947 49.528-38.783 82.032-64.401 104.947-82.653V400H48z"></path>
</svg> [farova@flu.cas.cz](mailto:farova@flu.cas.cz), <svg viewBox="0 0 512 512" style="height:1em;fill:currentColor;position:relative;display:inline-block;top:.1em;" xmlns="http://www.w3.org/2000/svg">
  <path d="M464 64H48C21.49 64 0 85.49 0 112v288c0 26.51 21.49 48 48 48h416c26.51 0 48-21.49 48-48V112c0-26.51-21.49-48-48-48zm0 48v40.805c-22.422 18.259-58.168 46.651-134.587 106.49-16.841 13.247-50.201 45.072-73.413 44.701-23.208.375-56.579-31.459-73.413-44.701C106.18 199.465 70.425 171.067 48 152.805V112h416zM48 400V214.398c22.914 18.251 55.409 43.862 104.938 82.646 21.857 17.205 60.134 55.186 103.062 54.955 42.717.231 80.509-37.199 103.053-54.947 49.528-38.783 82.032-64.401 104.947-82.653V400H48z"></path>
</svg> [hladik@flu.cas.cz](mailto:hladik@flu.cas.cz)
<svg viewBox="0 0 512 512" style="height:1em;fill:currentColor;position:relative;display:inline-block;top:.1em;" xmlns="http://www.w3.org/2000/svg">
  <path d="M459.37 151.716c.325 4.548.325 9.097.325 13.645 0 138.72-105.583 298.558-298.558 298.558-59.452 0-114.68-17.219-161.137-47.106 8.447.974 16.568 1.299 25.34 1.299 49.055 0 94.213-16.568 130.274-44.832-46.132-.975-84.792-31.188-98.112-72.772 6.498.974 12.995 1.624 19.818 1.624 9.421 0 18.843-1.3 27.614-3.573-48.081-9.747-84.143-51.98-84.143-102.985v-1.299c13.969 7.797 30.214 12.67 47.431 13.319-28.264-18.843-46.781-51.005-46.781-87.391 0-19.492 5.197-37.36 14.294-52.954 51.655 63.675 129.3 105.258 216.365 109.807-1.624-7.797-2.599-15.918-2.599-24.04 0-57.828 46.782-104.934 104.934-104.934 30.213 0 57.502 12.67 76.67 33.137 23.715-4.548 46.456-13.32 66.599-25.34-7.798 24.366-24.366 44.833-46.132 57.827 21.117-2.273 41.584-8.122 60.426-16.243-14.292 20.791-32.161 39.308-52.628 54.253z"></path>
</svg> [@nina_farova](https://twitter.com/nina_farova), <svg viewBox="0 0 512 512" style="height:1em;fill:currentColor;position:relative;display:inline-block;top:.1em;" xmlns="http://www.w3.org/2000/svg">
  <path d="M459.37 151.716c.325 4.548.325 9.097.325 13.645 0 138.72-105.583 298.558-298.558 298.558-59.452 0-114.68-17.219-161.137-47.106 8.447.974 16.568 1.299 25.34 1.299 49.055 0 94.213-16.568 130.274-44.832-46.132-.975-84.792-31.188-98.112-72.772 6.498.974 12.995 1.624 19.818 1.624 9.421 0 18.843-1.3 27.614-3.573-48.081-9.747-84.143-51.98-84.143-102.985v-1.299c13.969 7.797 30.214 12.67 47.431 13.319-28.264-18.843-46.781-51.005-46.781-87.391 0-19.492 5.197-37.36 14.294-52.954 51.655 63.675 129.3 105.258 216.365 109.807-1.624-7.797-2.599-15.918-2.599-24.04 0-57.828 46.782-104.934 104.934-104.934 30.213 0 57.502 12.67 76.67 33.137 23.715-4.548 46.456-13.32 66.599-25.34-7.798 24.366-24.366 44.833-46.132 57.827 21.117-2.273 41.584-8.122 60.426-16.243-14.292 20.791-32.161 39.308-52.628 54.253z"></path>
</svg> [@hlageek](https://twitter.com/hlageek)

<img src="https://owncloud.cesnet.cz/index.php/s/DumVoYT9IuQ6UEX/download" height="100">
<br>
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</svg> Czech Science Foundation project no. GJ20-01752Y, <i>Funded and Unfunded Research in the Czech Republic</i>

]

---

# RQs

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# Topic modeling approach

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.font110[

- dimensionality reduction technique for text data
 - terms are distributed across a limited number of topics
 - LDA most popular implementation, many variants
 - chosen method: TopSBM (Gerlach et al. 2018), network approach to topic modeling
- results based on 81571 publications

| Topic level| No. of topics|
|-----------:|-------------:|
|           1|          1993|
|           2|           965|
|           3|           327|
|           4|            52|

]
]

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.center[
<img src="https://topsbm.github.io/triangles.png" height=100>
<img src="https://topsbm.github.io/bipartite.png" height=300>
]
.font70[
<div style="position[@Gerlach2018]:absolute; bottom:50px;">
Gerlach, Martin, Tiago P. Peixoto, and Eduardo G. Altmann. 2018. “A Network Approach to Topic Models.” Science Advances 4 (7): eaaq1360. https://doi.org/10.1126/sciadv.aaq1360.
<br>
</div>]

]

---

# Initial approach to determine gender-distinctive topics

.left-column[

- 6624 projects with automatically assigned PI gender
- 327 topics
  - zero-corrected log odds
  - grant topics = average topics across grant related publications 
- 39 (11.9%) topics significant with respect to PI gender
  -  15 <svg viewBox="0 0 192 512" style="height:1em;fill:currentColor;position:relative;display:inline-block;top:.1em;" xmlns="http://www.w3.org/2000/svg">
  <path d="M96 0c35.346 0 64 28.654 64 64s-28.654 64-64 64-64-28.654-64-64S60.654 0 96 0m48 144h-11.36c-22.711 10.443-49.59 10.894-73.28 0H48c-26.51 0-48 21.49-48 48v136c0 13.255 10.745 24 24 24h16v136c0 13.255 10.745 24 24 24h64c13.255 0 24-10.745 24-24V352h16c13.255 0 24-10.745 24-24V192c0-26.51-21.49-48-48-48z"></path>
</svg> topics
  -  24 <svg viewBox="0 0 256 512" style="height:1em;fill:currentColor;position:relative;display:inline-block;top:.1em;" xmlns="http://www.w3.org/2000/svg">
  <path d="M128 0c35.346 0 64 28.654 64 64s-28.654 64-64 64c-35.346 0-64-28.654-64-64S92.654 0 128 0m119.283 354.179l-48-192A24 24 0 0 0 176 144h-11.36c-22.711 10.443-49.59 10.894-73.28 0H80a24 24 0 0 0-23.283 18.179l-48 192C4.935 369.305 16.383 384 32 384h56v104c0 13.255 10.745 24 24 24h32c13.255 0 24-10.745 24-24V384h56c15.591 0 27.071-14.671 23.283-29.821z"></path>
</svg> topics

]

.right-column[
**Predicting PI gender from the topic space of grants**
![](data:image/png;base64,#esa_2021-prez_files/figure-html/pi_topics_gender_plot1-1.png)

]

---

# Disadvantages

.left-column[

- type I errors in the high-dimensional space of topics
- insufficient number of observations for more granular topic models
- machine-learning models with feature importance metrics are preferable
- loss of information about unfunded topics

]

.right-column[

![](data:image/png;base64,#esa_2021-prez_files/figure-html/pi_topics_gender_plot2-1.png)

]

---

# Thank you for your attention

.font130[

#### Takeaways

- takeaway

#### Contact us:

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</svg> Nina Fárová, <svg viewBox="0 0 448 512" style="height:1em;fill:currentColor;position:relative;display:inline-block;top:.1em;" xmlns="http://www.w3.org/2000/svg">
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</svg> [@nina_farova](https://twitter.com/nina_farova), <svg viewBox="0 0 512 512" style="height:1em;fill:currentColor;position:relative;display:inline-block;top:.1em;" xmlns="http://www.w3.org/2000/svg">
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</svg> [@hlageek](https://twitter.com/hlageek)

]